
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import plotly as plotly
import plotly.express as px
plotly.offline.init_notebook_mode()

from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import RobustScaler
from sklearn.metrics import roc_curve, roc_auc_score
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC

from imblearn.over_sampling import SMOTE
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score


# Load the data
df = pd.read_csv('creditcard.csv')

df


df.describe()


# boxplot for v17 feature
plt.title('V17 feature before removing outliers')
sns.boxplot(x='Class', y='V17', data=df)
plt.show()

# boxplot for v14 feature
plt.title('V14 feature before removing outliers')
sns.boxplot(x='Class', y='V14', data=df)
plt.show()

#for amount
plt.title('Amount feature before removing outliers')
sns.boxplot(x='Class', y='Amount', data=df)
plt.show()


df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 284807 entries, 0 to 284806
Data columns (total 31 columns):
 #   Column  Non-Null Count   Dtype  
---  ------  --------------   -----  
 0   Time    284807 non-null  float64
 1   V1      284807 non-null  float64
 2   V2      284807 non-null  float64
 3   V3      284807 non-null  float64
 4   V4      284807 non-null  float64
 5   V5      284807 non-null  float64
 6   V6      284807 non-null  float64
 7   V7      284807 non-null  float64
 8   V8      284807 non-null  float64
 9   V9      284807 non-null  float64
 10  V10     284807 non-null  float64
 11  V11     284807 non-null  float64
 12  V12     284807 non-null  float64
 13  V13     284807 non-null  float64
 14  V14     284807 non-null  float64
 15  V15     284807 non-null  float64
 16  V16     284807 non-null  float64
 17  V17     284807 non-null  float64
 18  V18     284807 non-null  float64
 19  V19     284807 non-null  float64
 20  V20     284807 non-null  float64
 21  V21     284807 non-null  float64
 22  V22     284807 non-null  float64
 23  V23     284807 non-null  float64
 24  V24     284807 non-null  float64
 25  V25     284807 non-null  float64
 26  V26     284807 non-null  float64
 27  V27     284807 non-null  float64
 28  V28     284807 non-null  float64
 29  Amount  284807 non-null  float64
 30  Class   284807 non-null  int64  
dtypes: float64(30), int64(1)
memory usage: 67.4 MB


feature_list = ['Time', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11',
       'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20', 'V21',
       'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'Amount']


def calculate_outliers(df):
    cols = feature_list
    outliers_rows = []
    for col in cols:
        Q1 = df[col].quantile(0.25)
        Q3 = df[col].quantile(0.75)
        IQR = Q3 - Q1
        lower_bound = Q1 - 1.5 * IQR
        upper_bound = Q3 + 1.5 * IQR
        outliers = df[(df[col] < lower_bound) | (df[col] > upper_bound)]
        print(f'Number of outliers in {col}: {len(outliers)}, Percentage of outliers: {len(outliers) / len(df) * 100:.2f}%')
        outliers_rows.extend(outliers.index.tolist())
    
    # Get unique row indices with outliers
    outliers_rows = list(set(outliers_rows))
    print('\n')
    print(f'Number of rows with at least one outlier: {len(outliers_rows)}, Percentage of rows with at least one outlier: {len(outliers_rows) / df.shape[0] * 100:.2f}%')

    # Create boolean array indicating which rows have at least one outlier
    outliers_bool = df.index.isin(outliers_rows)
    
    return outliers_bool

iqr_outliers = calculate_outliers(df)

Number of outliers in Time: 0, Percentage of outliers: 0.00%
Number of outliers in V1: 7062, Percentage of outliers: 2.48%
Number of outliers in V2: 13526, Percentage of outliers: 4.75%
Number of outliers in V3: 3363, Percentage of outliers: 1.18%
Number of outliers in V4: 11148, Percentage of outliers: 3.91%
Number of outliers in V5: 12295, Percentage of outliers: 4.32%
Number of outliers in V6: 22965, Percentage of outliers: 8.06%
Number of outliers in V7: 8948, Percentage of outliers: 3.14%
Number of outliers in V8: 24134, Percentage of outliers: 8.47%
Number of outliers in V9: 8283, Percentage of outliers: 2.91%
Number of outliers in V10: 9496, Percentage of outliers: 3.33%
Number of outliers in V11: 780, Percentage of outliers: 0.27%
Number of outliers in V12: 15348, Percentage of outliers: 5.39%
Number of outliers in V13: 3368, Percentage of outliers: 1.18%
Number of outliers in V14: 14149, Percentage of outliers: 4.97%
Number of outliers in V15: 2894, Percentage of outliers: 1.02%
Number of outliers in V16: 8184, Percentage of outliers: 2.87%
Number of outliers in V17: 7420, Percentage of outliers: 2.61%
Number of outliers in V18: 7533, Percentage of outliers: 2.64%
Number of outliers in V19: 10205, Percentage of outliers: 3.58%
Number of outliers in V20: 27770, Percentage of outliers: 9.75%
Number of outliers in V21: 14497, Percentage of outliers: 5.09%
Number of outliers in V22: 1317, Percentage of outliers: 0.46%
Number of outliers in V23: 18541, Percentage of outliers: 6.51%
Number of outliers in V24: 4774, Percentage of outliers: 1.68%
Number of outliers in V25: 5367, Percentage of outliers: 1.88%
Number of outliers in V26: 5596, Percentage of outliers: 1.96%
Number of outliers in V27: 39163, Percentage of outliers: 13.75%
Number of outliers in V28: 30342, Percentage of outliers: 10.65%
Number of outliers in Amount: 31904, Percentage of outliers: 11.20%


Number of rows with at least one outlier: 138473, Percentage of rows with at least one outlier: 48.62%


no_outliers_df = df[~iqr_outliers]
no_outliers_df


# boxplot for v17 feature
plt.title('V17 feature after removing outliers')
sns.boxplot(x='Class', y='V17', data=no_outliers_df)
plt.show()

# boxplot for v14 feature
plt.title('V14 feature after removing outliers')
sns.boxplot(x='Class', y='V14', data=no_outliers_df)
plt.show()


no_outliers_df.describe()


# boxplot for v17 feature
plt.title('Time')
sns.boxplot(x='Class', y='Time', data=no_outliers_df)
plt.show()

# boxplot for v14 feature
plt.title('Amount')
sns.boxplot(x='Class', y='Amount', data=no_outliers_df)
plt.show()


sns.displot(no_outliers_df, x = "Amount", kde=True, bins=50, aspect=2)
plt.title('Price Distribution')
plt.xlabel('Price')
plt.ylabel('Density')
plt.show()


no_outliers_df_copy = no_outliers_df.copy()

scaler = RobustScaler()

no_outliers_df_copy['Time'] = scaler.fit_transform(no_outliers_df_copy['Time'].values.reshape(-1, 1))
no_outliers_df_copy['Amount'] = scaler.fit_transform(no_outliers_df_copy['Amount'].values.reshape(-1, 1))

no_outliers_df_copy


no_outliers_df_copy.describe()


cleaned_df = no_outliers_df_copy.copy()


class_distribution = cleaned_df['Class'].value_counts()
class_distribution

Class
0    146319
1        15
Name: count, dtype: int64


fig = px.pie(cleaned_df, names='Class', title='Class Distribution')
fig.show()

plt.figure(figsize=(8, 6))
sns.barplot(x=class_distribution.index, y=class_distribution)
plt.title('Class Distribution')
plt.xlabel('Class')
plt.ylabel('Frequency')
plt.show()


correlation = cleaned_df.corr()
display(round(correlation, 4))

#heatmap to visualize the correlation
plt.figure(figsize=(20, 20))
sns.heatmap(correlation, annot=True, cmap='coolwarm', fmt='.2f')
plt.title('Correlation Heatmap')
plt.show()


dfc = correlation['Class'].sort_values(ascending=False)
dfc = pd.DataFrame(dfc)
dfc


dfc.iloc[1:11]


correlations = cleaned_df.corr()['Class'].sort_values(ascending=False)

plt.figure(figsize=(10, 6))
sns.barplot(x=correlations.values, y=correlations.index)
plt.title('Feature Correlations with Target')
plt.show()


# top 10 correlated features with the target
top_10_correlated = correlations[1:11]
display(top_10_correlated)

# names of the top 10 correlated features
top_10_correlated_features = top_10_correlated.index
top_10_correlated_features

V4     0.009452
V17    0.008973
V2     0.007927
V5     0.006523
V16    0.006280
V7     0.005647
V18    0.004756
V11    0.004570
V25    0.003670
V3     0.001260
Name: Class, dtype: float64

Index(['V4', 'V17', 'V2', 'V5', 'V16', 'V7', 'V18', 'V11', 'V25', 'V3'], dtype='object')


fig, axes = plt.subplots(5, 2, figsize=(20, 30))
fig.suptitle('Top 10 Correlated Features with Target')

for i, feature in enumerate(top_10_correlated_features):
    sns.boxplot(ax=axes[i // 2, i % 2], x='Class', y=feature
                , data=cleaned_df, showfliers=True)
    axes[i // 2, i % 2].set_title(f'{feature} Distribution')
    axes[i // 2, i % 2].set_xlabel('Class')
    axes[i // 2, i % 2].set_ylabel(feature)
    
plt.show()


X = cleaned_df.drop('Class', axis=1)
y = cleaned_df['Class']

X


X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

X_train.shape, X_test.shape, y_train.shape, y_test.shape

((117067, 30), (29267, 30), (117067,), (29267,))


model = LogisticRegression(max_iter=1000)

scores = cross_val_score(model, X_train, y_train, cv=5)


model.fit(X_train, y_train)

LogisticRegression(max_iter=1000)

LogisticRegression(max_iter=1000)


print(model.coef_)

[[-0.74766799  0.28960949  0.44083742  0.36202213  0.23096235 -0.08531678
   0.25782693  0.30432583 -0.21070329  0.1894693  -0.53779737  0.58322328
  -0.36558853 -0.40378273 -1.56695142  0.65485352  0.71317105  0.76487635
   0.37601602 -0.14127806 -0.11411718 -0.12207981 -0.19829875 -0.36901884
  -0.19863475  0.29735889 -0.25192101 -0.0914607  -0.09562239  0.36756322]]


feature_names = X_train.columns


coefs=model.coef_[0]
top_ten = np.argpartition(coefs, -10)[-10:]
top_ten
print(feature_names[top_ten])

Index(['V25', 'Amount', 'V18', 'V3', 'V7', 'V2', 'V17', 'V16', 'V11', 'V15'], dtype='object')


feature_importance = pd.DataFrame({'Feature': X_train.columns, 'Importance': model.coef_[0]})
feature_importance = feature_importance.sort_values('Importance', ascending=False)
feature_importance


y_pred = model.predict(X_test)

print("Accuracy:", accuracy_score(y_test, y_pred))
print("Precision:", precision_score(y_test, y_pred, zero_division=0))
print("Recall:", recall_score(y_test, y_pred))
print("F1-Score:", f1_score(y_test, y_pred))

Accuracy: 0.9998291591211945
Precision: 0.0
Recall: 0.0
F1-Score: 0.0


y_pred_proba = model.predict_proba(X_test)[:, 1].ravel()  # probabilities for the positive outcome
y_pred = model.predict(X_test).ravel()  # predicted classes

# Create a DataFrame
df_predictions = pd.DataFrame({
    'probability': y_pred_proba,
    'prediction': y_pred,
    'ground_truth': y_test.values.flatten()
})

# Filter the DataFrame to only include instances where the ground truth was 1
df_predictions = df_predictions[df_predictions['ground_truth'] == 1]

df_predictions


y_pred_proba = model.predict_proba(X_test)[:,1]
fpr, tpr, _ = roc_curve(y_test, y_pred_proba)

plt.plot(fpr, tpr)
plt.title('ROC Curve')
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.show()

print("AUC:", roc_auc_score(y_test, y_pred_proba))

AUC: 0.543428337092475


smote = SMOTE(random_state=42)
X_res, y_res = smote.fit_resample(X, y)

X_train_res, X_test_res, y_train_res, y_test_res = train_test_split(X_res, y_res, test_size=0.2, random_state=42)

model.fit(X_train_res, y_train_res)
y_pred_res = model.predict(X_test_res)

print("Accuracy:", accuracy_score(y_test_res, y_pred_res))
print("Precision:", precision_score(y_test_res, y_pred_res, zero_division=0))
print("Recall:", recall_score(y_test_res, y_pred_res))
print("F1-Score:", f1_score(y_test_res, y_pred_res))

Accuracy: 0.96311167304538
Precision: 0.9316555872111427
Recall: 1.0
F1-Score: 0.964618737811573


knn = KNeighborsClassifier()
knn.fit(X_train, y_train)
y_pred_knn = knn.predict(X_test)

svm = SVC()
svm.fit(X_train, y_train)
y_pred_svm = svm.predict(X_test)

print("KNN Accuracy:", accuracy_score(y_test, y_pred_knn))
print("KNN Precision:", precision_score(y_test, y_pred_knn, zero_division=0))
print("KNN Recall:", recall_score(y_test, y_pred_knn))
print("SVM Accuracy:", accuracy_score(y_test, y_pred_svm))
print("SVM Precision:", precision_score(y_test, y_pred_svm, zero_division=0))
print("SVM Recall:", recall_score(y_test, y_pred_svm))

KNN Accuracy: 0.9998291591211945
KNN Precision: 0.0
KNN Recall: 0.0
SVM Accuracy: 0.9998291591211945
SVM Precision: 0.0
SVM Recall: 0.0

	Time	V1	V2	V3	V4	V5	V6	V7	V8	V9	...	V21	V22	V23	V24	V25	V26	V27	V28	Amount	Class
0	0.0	-1.359807	-0.072781	2.536347	1.378155	-0.338321	0.462388	0.239599	0.098698	0.363787	...	-0.018307	0.277838	-0.110474	0.066928	0.128539	-0.189115	0.133558	-0.021053	149.62	0
1	0.0	1.191857	0.266151	0.166480	0.448154	0.060018	-0.082361	-0.078803	0.085102	-0.255425	...	-0.225775	-0.638672	0.101288	-0.339846	0.167170	0.125895	-0.008983	0.014724	2.69	0
2	1.0	-1.358354	-1.340163	1.773209	0.379780	-0.503198	1.800499	0.791461	0.247676	-1.514654	...	0.247998	0.771679	0.909412	-0.689281	-0.327642	-0.139097	-0.055353	-0.059752	378.66	0
3	1.0	-0.966272	-0.185226	1.792993	-0.863291	-0.010309	1.247203	0.237609	0.377436	-1.387024	...	-0.108300	0.005274	-0.190321	-1.175575	0.647376	-0.221929	0.062723	0.061458	123.50	0
4	2.0	-1.158233	0.877737	1.548718	0.403034	-0.407193	0.095921	0.592941	-0.270533	0.817739	...	-0.009431	0.798278	-0.137458	0.141267	-0.206010	0.502292	0.219422	0.215153	69.99	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
284802	172786.0	-11.881118	10.071785	-9.834783	-2.066656	-5.364473	-2.606837	-4.918215	7.305334	1.914428	...	0.213454	0.111864	1.014480	-0.509348	1.436807	0.250034	0.943651	0.823731	0.77	0
284803	172787.0	-0.732789	-0.055080	2.035030	-0.738589	0.868229	1.058415	0.024330	0.294869	0.584800	...	0.214205	0.924384	0.012463	-1.016226	-0.606624	-0.395255	0.068472	-0.053527	24.79	0
284804	172788.0	1.919565	-0.301254	-3.249640	-0.557828	2.630515	3.031260	-0.296827	0.708417	0.432454	...	0.232045	0.578229	-0.037501	0.640134	0.265745	-0.087371	0.004455	-0.026561	67.88	0
284805	172788.0	-0.240440	0.530483	0.702510	0.689799	-0.377961	0.623708	-0.686180	0.679145	0.392087	...	0.265245	0.800049	-0.163298	0.123205	-0.569159	0.546668	0.108821	0.104533	10.00	0
284806	172792.0	-0.533413	-0.189733	0.703337	-0.506271	-0.012546	-0.649617	1.577006	-0.414650	0.486180	...	0.261057	0.643078	0.376777	0.008797	-0.473649	-0.818267	-0.002415	0.013649	217.00	0

	Time	V1	V2	V3	V4	V5	V6	V7	V8	V9	...	V21	V22	V23	V24	V25	V26	V27	V28	Amount	Class
count	284807.000000	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	...	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	284807.000000	284807.000000
mean	94813.859575	1.168375e-15	3.416908e-16	-1.379537e-15	2.074095e-15	9.604066e-16	1.487313e-15	-5.556467e-16	1.213481e-16	-2.406331e-15	...	1.654067e-16	-3.568593e-16	2.578648e-16	4.473266e-15	5.340915e-16	1.683437e-15	-3.660091e-16	-1.227390e-16	88.349619	0.001727
std	47488.145955	1.958696e+00	1.651309e+00	1.516255e+00	1.415869e+00	1.380247e+00	1.332271e+00	1.237094e+00	1.194353e+00	1.098632e+00	...	7.345240e-01	7.257016e-01	6.244603e-01	6.056471e-01	5.212781e-01	4.822270e-01	4.036325e-01	3.300833e-01	250.120109	0.041527
min	0.000000	-5.640751e+01	-7.271573e+01	-4.832559e+01	-5.683171e+00	-1.137433e+02	-2.616051e+01	-4.355724e+01	-7.321672e+01	-1.343407e+01	...	-3.483038e+01	-1.093314e+01	-4.480774e+01	-2.836627e+00	-1.029540e+01	-2.604551e+00	-2.256568e+01	-1.543008e+01	0.000000	0.000000
25%	54201.500000	-9.203734e-01	-5.985499e-01	-8.903648e-01	-8.486401e-01	-6.915971e-01	-7.682956e-01	-5.540759e-01	-2.086297e-01	-6.430976e-01	...	-2.283949e-01	-5.423504e-01	-1.618463e-01	-3.545861e-01	-3.171451e-01	-3.269839e-01	-7.083953e-02	-5.295979e-02	5.600000	0.000000
50%	84692.000000	1.810880e-02	6.548556e-02	1.798463e-01	-1.984653e-02	-5.433583e-02	-2.741871e-01	4.010308e-02	2.235804e-02	-5.142873e-02	...	-2.945017e-02	6.781943e-03	-1.119293e-02	4.097606e-02	1.659350e-02	-5.213911e-02	1.342146e-03	1.124383e-02	22.000000	0.000000
75%	139320.500000	1.315642e+00	8.037239e-01	1.027196e+00	7.433413e-01	6.119264e-01	3.985649e-01	5.704361e-01	3.273459e-01	5.971390e-01	...	1.863772e-01	5.285536e-01	1.476421e-01	4.395266e-01	3.507156e-01	2.409522e-01	9.104512e-02	7.827995e-02	77.165000	0.000000
max	172792.000000	2.454930e+00	2.205773e+01	9.382558e+00	1.687534e+01	3.480167e+01	7.330163e+01	1.205895e+02	2.000721e+01	1.559499e+01	...	2.720284e+01	1.050309e+01	2.252841e+01	4.584549e+00	7.519589e+00	3.517346e+00	3.161220e+01	3.384781e+01	25691.160000	1.000000

	Time	V1	V2	V3	V4	V5	V6	V7	V8	V9	...	V21	V22	V23	V24	V25	V26	V27	V28	Amount	Class
0	0.0	-1.359807	-0.072781	2.536347	1.378155	-0.338321	0.462388	0.239599	0.098698	0.363787	...	-0.018307	0.277838	-0.110474	0.066928	0.128539	-0.189115	0.133558	-0.021053	149.62	0
1	0.0	1.191857	0.266151	0.166480	0.448154	0.060018	-0.082361	-0.078803	0.085102	-0.255425	...	-0.225775	-0.638672	0.101288	-0.339846	0.167170	0.125895	-0.008983	0.014724	2.69	0
3	1.0	-0.966272	-0.185226	1.792993	-0.863291	-0.010309	1.247203	0.237609	0.377436	-1.387024	...	-0.108300	0.005274	-0.190321	-1.175575	0.647376	-0.221929	0.062723	0.061458	123.50	0
4	2.0	-1.158233	0.877737	1.548718	0.403034	-0.407193	0.095921	0.592941	-0.270533	0.817739	...	-0.009431	0.798278	-0.137458	0.141267	-0.206010	0.502292	0.219422	0.215153	69.99	0
5	2.0	-0.425966	0.960523	1.141109	-0.168252	0.420987	-0.029728	0.476201	0.260314	-0.568671	...	-0.208254	-0.559825	-0.026398	-0.371427	-0.232794	0.105915	0.253844	0.081080	3.67	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
284796	172780.0	1.884849	-0.143540	-0.999943	1.506772	-0.035300	-0.613638	0.190241	-0.249058	0.666458	...	0.144008	0.634646	-0.042114	-0.053206	0.316403	-0.461441	0.018265	-0.041068	60.00	0
284797	172782.0	-0.241923	0.712247	0.399806	-0.463406	0.244531	-1.343668	0.929369	-0.206210	0.106234	...	-0.228876	-0.514376	0.279598	0.371441	-0.559238	0.113144	0.131507	0.081265	5.49	0
284800	172784.0	2.039560	-0.175233	-1.196825	0.234580	-0.008713	-0.726571	0.017050	-0.118228	0.435402	...	-0.268048	-0.717211	0.297930	-0.359769	-0.315610	0.201114	-0.080826	-0.075071	2.68	0
284801	172785.0	0.120316	0.931005	-0.546012	-0.745097	1.130314	-0.235973	0.812722	0.115093	-0.204064	...	-0.314205	-0.808520	0.050343	0.102800	-0.435870	0.124079	0.217940	0.068803	2.69	0
284803	172787.0	-0.732789	-0.055080	2.035030	-0.738589	0.868229	1.058415	0.024330	0.294869	0.584800	...	0.214205	0.924384	0.012463	-1.016226	-0.606624	-0.395255	0.068472	-0.053527	24.79	0

	Time	V1	V2	V3	V4	V5	V6	V7	V8	V9	...	V21	V22	V23	V24	V25	V26	V27	V28	Amount	Class
count	146334.000000	146334.000000	146334.000000	146334.000000	146334.000000	146334.000000	146334.000000	146334.000000	146334.000000	146334.000000	...	146334.000000	146334.000000	146334.000000	146334.000000	146334.000000	146334.000000	146334.000000	146334.000000	146334.000000	146334.000000
mean	95576.533109	0.689479	0.065514	0.151846	0.008564	-0.132735	-0.377081	0.011083	0.031417	-0.065259	...	-0.037541	-0.038136	0.007977	-0.000771	0.026885	-0.015648	0.018484	0.009894	34.895958	0.000103
std	46781.425189	1.191887	0.759219	1.179382	1.067758	0.764318	0.640091	0.680457	0.309031	0.866178	...	0.242236	0.662272	0.194051	0.519107	0.422551	0.430137	0.111440	0.079614	41.822617	0.010124
min	0.000000	-4.000703	-2.629561	-3.763535	-3.234792	-2.644680	-2.501379	-2.238746	-1.011230	-2.503120	...	-0.849287	-2.132206	-0.625903	-1.544928	-1.318690	-1.178460	-0.313658	-0.249694	0.000000	0.000000
25%	55233.250000	-0.447437	-0.432748	-0.657034	-0.726678	-0.638769	-0.818151	-0.474484	-0.176317	-0.583642	...	-0.227876	-0.583887	-0.117911	-0.333400	-0.287113	-0.314764	-0.048077	-0.043591	4.380000	0.000000
50%	81701.000000	1.132213	0.051078	0.275632	0.114266	-0.132104	-0.440719	0.054474	-0.004907	-0.063699	...	-0.050773	-0.043178	0.001174	0.035755	0.040798	-0.041862	0.003430	0.009687	16.570000	0.000000
75%	140376.000000	1.869928	0.642562	1.029075	0.691059	0.376857	0.007746	0.492660	0.196328	0.477281	...	0.141951	0.459555	0.131522	0.392546	0.348641	0.203760	0.064287	0.043134	50.000000	0.000000
max	172787.000000	2.454930	2.871336	3.790995	3.131204	2.565293	2.146309	2.251729	1.131305	2.456673	...	0.808491	2.118715	0.611675	1.299212	1.350739	1.092785	0.333860	0.275066	184.500000	1.000000

	Time	V1	V2	V3	V4	V5	V6	V7	V8	V9	...	V21	V22	V23	V24	V25	V26	V27	V28	Amount	Class
0	-0.959577	-1.359807	-0.072781	2.536347	1.378155	-0.338321	0.462388	0.239599	0.098698	0.363787	...	-0.018307	0.277838	-0.110474	0.066928	0.128539	-0.189115	0.133558	-0.021053	2.916484	0
1	-0.959577	1.191857	0.266151	0.166480	0.448154	0.060018	-0.082361	-0.078803	0.085102	-0.255425	...	-0.225775	-0.638672	0.101288	-0.339846	0.167170	0.125895	-0.008983	0.014724	-0.304253	0
3	-0.959565	-0.966272	-0.185226	1.792993	-0.863291	-0.010309	1.247203	0.237609	0.377436	-1.387024	...	-0.108300	0.005274	-0.190321	-1.175575	0.647376	-0.221929	0.062723	0.061458	2.343928	0
4	-0.959553	-1.158233	0.877737	1.548718	0.403034	-0.407193	0.095921	0.592941	-0.270533	0.817739	...	-0.009431	0.798278	-0.137458	0.141267	-0.206010	0.502292	0.219422	0.215153	1.170978	0
5	-0.959553	-0.425966	0.960523	1.141109	-0.168252	0.420987	-0.029728	0.476201	0.260314	-0.568671	...	-0.208254	-0.559825	-0.026398	-0.371427	-0.232794	0.105915	0.253844	0.081080	-0.282771	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
284796	1.069721	1.884849	-0.143540	-0.999943	1.506772	-0.035300	-0.613638	0.190241	-0.249058	0.666458	...	0.144008	0.634646	-0.042114	-0.053206	0.316403	-0.461441	0.018265	-0.041068	0.951995	0
284797	1.069745	-0.241923	0.712247	0.399806	-0.463406	0.244531	-1.343668	0.929369	-0.206210	0.106234	...	-0.228876	-0.514376	0.279598	0.371441	-0.559238	0.113144	0.131507	0.081265	-0.242876	0
284800	1.069768	2.039560	-0.175233	-1.196825	0.234580	-0.008713	-0.726571	0.017050	-0.118228	0.435402	...	-0.268048	-0.717211	0.297930	-0.359769	-0.315610	0.201114	-0.080826	-0.075071	-0.304472	0
284801	1.069780	0.120316	0.931005	-0.546012	-0.745097	1.130314	-0.235973	0.812722	0.115093	-0.204064	...	-0.314205	-0.808520	0.050343	0.102800	-0.435870	0.124079	0.217940	0.068803	-0.304253	0
284803	1.069803	-0.732789	-0.055080	2.035030	-0.738589	0.868229	1.058415	0.024330	0.294869	0.584800	...	0.214205	0.924384	0.012463	-1.016226	-0.606624	-0.395255	0.068472	-0.053527	0.180184	0

Importing the libraries¶

Getting the Data¶

Data Preprocessing Tasks¶

Detect and handle outliers for all the numerical features in the dataset using either the Whiskers Approach, or Z-score approach.¶

If needed, normalize numerical features using appropriate method based on feature characteristics.¶

Descriptive Analytics Tasks¶

Analyze the distribution of fraud vs non-fraud transactions and comment on it.¶

Analyze the correlation between fraud/non-fraud transactions and all the other numerical features.¶

ML Model Training and Testing Tasks¶

Split the cleaned data into training and testing sets (e.g., 80% training, 20% testing).¶

Use 5-fold cross-validation to train and validate the performance of all the models in this section.¶

Evaluating the Model with the Test Set¶

Handling Class Imbalance¶

Evaluating the model with KNN and SVM.¶

	Time	V1	V2	V3	V4	V5	V6	V7	V8	V9	...	V21	V22	V23	V24	V25	V26	V27	V28	Amount	Class
Time	1.0000	0.2433	-0.0790	-0.5843	-0.2062	0.2744	-0.1706	0.1115	-0.1860	0.1469	...	0.0721	0.1124	0.2391	-0.0003	-0.3268	-0.0553	-0.0675	-0.2024	-0.0290	-0.0048
V1	0.2433	1.0000	-0.6076	-0.6536	0.1398	-0.3027	-0.0689	-0.4339	-0.4521	0.2883	...	-0.0852	-0.0061	0.3593	-0.0115	0.1505	-0.0060	-0.2669	-0.3880	0.0078	-0.0022
V2	-0.0790	-0.6076	1.0000	0.1823	0.2804	0.5842	-0.1425	0.7692	0.1306	-0.0739	...	-0.0428	-0.1154	-0.2657	0.0413	-0.0911	0.0162	0.2058	0.2990	-0.3911	0.0079
V3	-0.5843	-0.6536	0.1823	1.0000	0.0400	-0.2696	0.3302	-0.0865	0.4176	-0.1229	...	-0.0075	-0.0274	-0.2511	0.0998	0.0022	-0.0284	0.2603	0.3422	-0.0024	0.0013
V4	-0.2062	0.1398	0.2804	0.0400	1.0000	0.1120	0.1844	0.1019	0.0484	0.2839	...	-0.0113	-0.0382	0.0208	0.0818	0.1901	-0.1013	-0.0443	0.0126	-0.0442	0.0095
V5	0.2744	-0.3027	0.5842	-0.2696	0.1120	1.0000	-0.1283	0.7820	-0.1964	-0.0433	...	0.0024	-0.0579	-0.2347	-0.2112	-0.0318	0.0180	0.0135	0.0533	-0.2647	0.0065
V6	-0.1706	-0.0689	-0.1425	0.3302	0.1844	-0.1283	1.0000	-0.3883	0.5862	0.1179	...	0.0377	0.0833	-0.1003	-0.4003	0.0024	-0.0658	0.1300	0.0225	0.1575	0.0010
V7	0.1115	-0.4339	0.7692	-0.0865	0.1019	0.7820	-0.3883	1.0000	-0.3350	-0.1210	...	-0.0445	-0.1038	-0.2654	0.0418	-0.0263	0.0318	0.0402	0.1493	-0.1172	0.0056
V8	-0.1860	-0.4521	0.1306	0.4176	0.0484	-0.1964	0.5862	-0.3350	1.0000	0.0242	...	0.0796	0.0530	-0.0206	-0.1525	-0.1515	-0.0206	0.2312	0.1944	0.0330	-0.0010
V9	0.1469	0.2883	-0.0739	-0.1229	0.2839	-0.0433	0.1179	-0.1210	0.0242	1.0000	...	0.0174	0.0546	0.1690	0.0229	-0.0454	0.0589	-0.0420	-0.1647	-0.0507	-0.0041
V10	-0.0079	0.4450	-0.5536	-0.1738	-0.0873	-0.4234	0.0838	-0.4776	-0.2174	-0.4706	...	-0.0521	0.0490	0.1750	-0.0477	0.0373	-0.0781	-0.0776	-0.2171	0.1145	-0.0035
V11	-0.2363	0.0197	-0.0108	0.0740	-0.0064	-0.0305	0.0997	-0.0556	0.1220	-0.1410	...	0.0489	-0.0004	0.0692	0.1195	0.0131	0.0209	-0.0409	-0.0361	-0.0250	0.0046
V12	0.0322	0.0694	0.1483	-0.0424	0.2686	0.1197	0.1373	0.0887	0.0918	0.2425	...	-0.0194	-0.0219	0.0962	0.0583	0.0213	0.0043	-0.0037	-0.0344	-0.0476	-0.0034
V13	0.0132	0.0571	0.0198	-0.0096	-0.0723	0.0247	0.0009	-0.0095	-0.2116	-0.1469	...	-0.0487	0.0287	0.0030	-0.0318	0.0043	0.0053	0.0088	0.0132	-0.0129	-0.0035
V14	-0.0984	-0.0060	0.1206	-0.0893	0.1204	0.0910	-0.1211	0.1783	0.0352	-0.0224	...	0.0840	-0.0007	-0.0018	-0.0424	0.0779	0.0050	-0.1108	-0.0572	-0.0237	-0.0126
V15	-0.2669	0.0538	0.0623	0.1119	0.0776	-0.1427	-0.1711	-0.0468	-0.1136	0.0219	...	0.0515	-0.0115	0.0454	0.0029	0.0434	-0.0010	0.0021	0.0529	-0.0528	0.0010
V16	0.0050	0.0113	0.0262	0.0090	-0.1122	-0.0072	-0.0114	-0.1135	0.1031	-0.0127	...	0.2137	0.0381	0.0017	-0.1024	-0.0520	0.0085	-0.0363	0.0066	-0.0746	0.0063
V17	-0.1028	-0.0210	-0.0569	-0.0318	-0.0089	-0.1654	-0.0615	-0.1306	0.0936	-0.1441	...	-0.0374	0.0143	0.0493	0.2137	0.0402	-0.0163	0.0519	0.0890	0.0496	0.0090
V18	0.1231	-0.0444	-0.0095	-0.0257	-0.0157	0.0033	0.1187	-0.0927	0.1484	0.0120	...	-0.0091	0.0519	-0.1221	-0.1144	-0.0663	0.0157	0.0239	0.0149	0.0688	0.0048
V19	0.0923	0.0377	-0.0235	-0.1318	-0.1196	0.0398	0.0366	0.0009	-0.0189	0.0442	...	0.0092	-0.0205	-0.0786	-0.1737	-0.0139	-0.0303	-0.0210	-0.0577	-0.0246	-0.0083
V20	-0.1935	-0.3776	0.2207	0.3038	0.0058	0.2024	0.0405	0.2489	0.0395	-0.0200	...	0.2021	0.0808	-0.2614	-0.0077	0.0512	0.0354	0.2357	0.1615	0.2519	0.0000
V21	0.0721	-0.0852	-0.0428	-0.0075	-0.0113	0.0024	0.0377	-0.0445	0.0796	0.0174	...	1.0000	0.9201	-0.4082	0.0750	0.1383	-0.0659	-0.0100	0.0371	0.1522	0.0010
V22	0.1124	-0.0061	-0.1154	-0.0274	-0.0382	-0.0579	0.0833	-0.1038	0.0530	0.0546	...	0.9201	1.0000	-0.4086	0.0733	0.1227	-0.0649	0.0316	-0.0260	0.0860	0.0005
V23	0.2391	0.3593	-0.2657	-0.2511	0.0208	-0.2347	-0.1003	-0.2654	-0.0206	0.1690	...	-0.4082	-0.4086	1.0000	0.2037	-0.5813	-0.0254	-0.0557	-0.1023	-0.0177	-0.0061
V24	-0.0003	-0.0115	0.0413	0.0998	0.0818	-0.2112	-0.4003	0.0418	-0.1525	0.0229	...	0.0750	0.0733	0.2037	1.0000	-0.0647	0.0169	-0.0464	0.0632	-0.0153	0.0003
V25	-0.3268	0.1505	-0.0911	0.0022	0.1901	-0.0318	0.0024	-0.0263	-0.1515	-0.0454	...	0.1383	0.1227	-0.5813	-0.0647	1.0000	-0.0603	-0.1731	-0.1460	0.0352	0.0037
V26	-0.0553	-0.0060	0.0162	-0.0284	-0.1013	0.0180	-0.0658	0.0318	-0.0206	0.0589	...	-0.0659	-0.0649	-0.0254	0.0169	-0.0603	1.0000	-0.2055	-0.0431	-0.0372	-0.0032
V27	-0.0675	-0.2669	0.2058	0.2603	-0.0443	0.0135	0.1300	0.0402	0.2312	-0.0420	...	-0.0100	0.0316	-0.0557	-0.0464	-0.1731	-0.2055	1.0000	0.5737	-0.1216	-0.0029
V28	-0.2024	-0.3880	0.2990	0.3422	0.0126	0.0533	0.0225	0.1493	0.1944	-0.1647	...	0.0371	-0.0260	-0.1023	0.0632	-0.1460	-0.0431	0.5737	1.0000	-0.0168	-0.0006
Amount	-0.0290	0.0078	-0.3911	-0.0024	-0.0442	-0.2647	0.1575	-0.1172	0.0330	-0.0507	...	0.1522	0.0860	-0.0177	-0.0153	0.0352	-0.0372	-0.1216	-0.0168	1.0000	-0.0023
Class	-0.0048	-0.0022	0.0079	0.0013	0.0095	0.0065	0.0010	0.0056	-0.0010	-0.0041	...	0.0010	0.0005	-0.0061	0.0003	0.0037	-0.0032	-0.0029	-0.0006	-0.0023	1.0000

	Class
Class	1.000000
V4	0.009452
V17	0.008973
V2	0.007927
V5	0.006523
V16	0.006280
V7	0.005647
V18	0.004756
V11	0.004570
V25	0.003670
V3	0.001260
V21	0.001031
V15	0.000998
V6	0.000990
V22	0.000456
V24	0.000291
V20	0.000007
V28	-0.000618
V8	-0.001003
V1	-0.002218
Amount	-0.002288
V27	-0.002895
V26	-0.003176
V12	-0.003404
V13	-0.003517
V10	-0.003546
V9	-0.004078
Time	-0.004837
V23	-0.006103
V19	-0.008324
V14	-0.012567

	Feature	Importance
17	V17	0.764876
16	V16	0.713171
15	V15	0.654854
11	V11	0.583223
2	V2	0.440837
18	V18	0.376016
29	Amount	0.367563
3	V3	0.362022
7	V7	0.304326
25	V25	0.297359
1	V1	0.289609
6	V6	0.257827
4	V4	0.230962
9	V9	0.189469
5	V5	-0.085317
27	V27	-0.091461
28	V28	-0.095622
20	V20	-0.114117
21	V21	-0.122080
19	V19	-0.141278
22	V22	-0.198299
24	V24	-0.198635
8	V8	-0.210703
26	V26	-0.251921
12	V12	-0.365589
23	V23	-0.369019
13	V13	-0.403783
10	V10	-0.537797
0	Time	-0.747668
14	V14	-1.566951

	probability	ground_truth
2849	2.837467e-03	1
13983	3.925579e-07	1
15391	6.250653e-04	1
15487	2.360204e-06	1
27704	1.599608e-05	1